Overview

Now that the 2020 election is officially over and Biden was elected as the President of the United States, it is important that I reflect on my prediction model. I am excited to see how I cold learn from my model for future models that I create.

Model Recap and Predictions

Let’s first recap on my prediction model to get a better picture of what it was.

Patterns and Accuracy

Overall, I am pretty satisfied with how my model turned out. While I did miss a few states, I was quite happy that predicted some battleground states correctly.

Above is a comparison between my predictions and the actual results of the 2020 election. As you can see, the states that I got wrong were battleground states. However, I would like to say that the predictive intervals for the battleground states did capture the true result.

Moreover, let’s take a look into the plot above, which plots the actual two-party vote share for Trump against my predictions for Trump. The blue points represent states Biden won and the red points represent states Trump won.

Furthermore, the map above shows the difference between Trump’s actual and predicted two party vote share in each state. A negative difference means that Trump was overpredicted for that particular state while a positive difference means that Trump was underpredicted for that particular state. I will say that it is interesting where Trump was greatly overpredicted or greatly underpredicted are states that are not battleground states. This makes sense because states that are traditionally red or blue and not battleground typically have less polling as there is a small chance that those states will flip. This is why we may see a state like Alaska with little polling where Trump is greatly overpredicted there.

The above historgram shows the error distribution for my prediction model and the model’s average error seems to be mainly normally distributed around 0.